File size: 2,010 Bytes
1bc3bb2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
---
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- Dev372/Medical_STT_Dataset_1.1
metrics:
- wer
model-index:
- name: OutcomesAI-Whisper-tiny-v1.0
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Medical_STT_Dataset_1.1
type: Dev372/Medical_STT_Dataset_1.1
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 7.224272510532676
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# OutcomesAI-Whisper-tiny-v1.0
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Medical_STT_Dataset_1.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1675
- Wer: 7.2243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.1067 | 2.5126 | 1000 | 0.1600 | 7.2308 |
| 0.0329 | 5.0251 | 2000 | 0.1479 | 6.5809 |
| 0.0131 | 7.5377 | 3000 | 0.1596 | 7.4104 |
| 0.0192 | 10.0503 | 4000 | 0.1675 | 7.2243 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|